Build, implement and scale distributed deep learning models for large-scale datasets
Deep Learning involves extracting features and insights from multiple layers of the data. This book will teach you how to deploy the deep learning networks with Hadoop.
Starting with understanding what deep learning is and what the various models associated with deep learning are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with Hadoop. The book will also show you how you can implement and parallelize Deep Belief Networks, CNN, RNN, RBM and much more using the popular deep learning library deeplearning4j. Get in depth mathematical explanations, visual representations to understand the implementation of Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you how you can implement image classification, audio processing and natural language processing on Hadoop.
By the end of this book, you will know how to deploy deep learning in distributed systems using Hadoop
What you will learn

• Explore Deep Learning and various models associated with it.
• Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it
• Implement Convolutional Neural Network (CNN) with deeplearning4j
• Delve into the implementation of Restricted Boltzmann Machines (RBM)
• Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN)
• Get hands on practice of deep learning and their implementation with Hadoop.
  

Table of Contents

1 Introduction to Deep Learning
2 Distributed Deep Learning for Large-Scale Data
3 Convolutional Neural Network
4 Recurrent Neural Network
5 Restricted Boltzmann Machines
6 Autoencoders
7 Miscellaneous Deep Learning Operations using Hadoop
8 References

deep-learning-hadoop.pdf
大小:(7.54 MB)

只需: 8 个论坛币  马上下载